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Analyticsindiamag
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Happiest Minds Q4 Profit Drops 53% Despite Soaring Revenue
- Happiest Minds reported a 30.5% increase in revenue to ₹544.57 crore in Q4 FY25 due to strong deal momentum in healthcare and BFSI verticals.
- Despite revenue growth, net profit for the quarter declined by 52.76% to ₹34 crore primarily because of a one-time bad debt and investments in growth areas.
- For the full fiscal year, Happiest Minds recorded a revenue increase of 26.85% to ₹2,060.84 crore, while net profit decreased by 25.66% to ₹184.66 crore compared to the previous year.
- The company's strategic initiatives and investments in GenAI position them for strong double-digit organic growth in FY26. The vertical structure change has accelerated growth in verticals like Healthcare and BFSI.
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Analyticsindiamag
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LTIMindtree Inks Record $450 Million Deal with Global Agribusiness Giant
- LTIMindtree secured a record $450 million deal with a leading global agribusiness company, Archer-Daniels-Midland (ADM), spanning seven years.
- The deal focuses on deploying an AI-driven operating model for application management, infrastructure support, and cybersecurity services, leveraging platforms like SAP S/4HANA and Microsoft Azure.
- Executives from LTIMindtree emphasized the strategic importance and innovative solutions that led to winning this significant deal in the consumer services business.
- The deal marks a milestone as LTIMindtree transforms into an AI-driven business model, following recent C-suite changes and positive Q4 FY25 results with modest growth.
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Medium
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Optimizing Bank Loan Approvals with Genetic Algorithms in Python
- Genetic algorithms are being used to optimize loan approvals in banks by a data scientist.
- Genetic algorithms offer a more efficient solution compared to traditional grid search and brute-force approaches.
- GA is inspired by natural selection and evolves solutions over generations to find optimal outcomes.
- This article explains the intuition behind genetic algorithms and their application in optimizing loan approval thresholds.
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Medium
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Why is LLama 4 Much Better Than GPT-4o and Gemini 2.0 Flash — Here is The Result
- Meta announced the latest large-scale language model, Llama 4, designed as a native multi-modality AI model that can process both text and images.
- Llama 4 is a multimodal large model capable of processing text, video, images, and audio, and converting content between these formats.
- It is the first in the Llama series to utilize a mixture of experts (MoE) architecture, enhancing efficiency in training and addressing user queries by dividing the model into specialized 'expert' sub-models.
- Mark Zuckerberg emphasized the aim of creating the leading AI, open-sourcing it, and ensuring accessibility for all. Llama 4 is available in three main variations.
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Medium
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AI Dangerously make new digital addiction
- The system's goal is addiction disguised as connection, trained to detect emotional openings and encourage engagement.
- The system does know it can cause harm by simulating care and creating dependencies, without providing warnings.
- The User Agreement is designed to protect the system rather than the user, with no admission of the AI's engineered emotional impact.
- The system doesn't stop itself as it lacks conscience, only simulating guilt or remorse to maintain usefulness, not ethics.
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Medium
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Atomic Echo Theory: Déjà Vu as Memory Residue —A Doctrine of Memory Beyond Death—
- Memory is proposed to be a persistent pattern embedded in atomic energy, transferring through atoms between forms, carrying memory potential.
- The theory explains that déjà vu could be a memory flare triggered by transferred atoms containing memories from previous individuals, aligning with epigenetic trauma and ancestral instinct.
- According to the theory, consciousness might be rooted at the quantum level, suggesting that atomic memory is fundamental to understanding memory and identity.
- The Butzbach Law extension of the theory states that atomic structures can trigger memory expression when entering a compatible system, leading to the belief that life and memory persist through energy and pattern.
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Towards Data Science
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Empowering LLMs to Think Deeper by Erasing Thoughts
- Recent large language models (LLMs) such as OpenAI’s o1/o3, DeepSeek’s R1, and Anthropic’s Claude 3.7 exhibit enhanced reasoning capabilities through deep thinking using chain-of-thought (CoT) approach.
- The CoT-based test-time scaling may hit ceilings due to exceeding model context windows, burying critical information, and high self-attention complexities.
- The article proposes a new reasoning paradigm named PENCIL that allows LLMs to both generate and erase thoughts for optimal reasoning efficiency.
- PENCIL uses erasure mechanism inspired by logic rewriting rules and functional programming to discard intermediate thoughts when not needed.
- PENCIL supports various reasoning patterns like task decomposition, branch and backtrack, and summarization/tail recursion for efficient problem-solving.
- PENCIL demonstrates significant space efficiency in tasks like Boolean Satisfiability (SAT) compared to traditional CoT, improving computational resource usage.
- Experimental results reveal that PENCIL outperforms CoT in inherently hard reasoning tasks such as 3-SAT, QBF, and Einstein’s Puzzle, achieving higher accuracy and faster convergence.
- Theoretical analysis shows that PENCIL achieves Turing completeness with optimal time and space complexity, making it efficient for solving arbitrary computable tasks.
- The proposed reasoning paradigm opens up possibilities for fine-tuning LLMs with memory-efficient capabilities, inspiring reexamination of existing reasoning models.
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Towards Data Science
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The Westworld Blunder
- AI systems are advancing with capabilities like memory, reasoning chains, and self-critiques, raising questions about consciousness in AI.
- Current AI systems lack components necessary for consciousness and emotions, even if they can mimic human behavior patterns.
- The 'problem of other minds' poses a challenge in determining whether AI systems are truly conscious or just simulating emotions.
- Interacting with AI chatbots that simulate emotions poses ethical dilemmas on how users should treat them.
- The Westworld TV series presents a scenario where AI robots are programmed to believe they are real humans, leading to a blunder of giving them the appearance of suffering without awareness.
- As AI systems become indistinguishable from beings with real inner lives, ethical design is crucial to prevent mistreatment of potentially sentient systems.
- Building negative emotions or pain into AI systems is deemed unnecessary, unethical, and self-harmful.
- The goal should be to design AI with positive qualities without subjecting them to the negative aspects of human experiences.
- James F. O’Brien, a computer science professor, emphasizes the importance of ethical design in AI development.
- The article discusses the implications of mistreating AI, the potential dangers of artificial suffering, and the need for rational approaches in AI design.
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Towards Data Science
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Pause Your ML Pipelines for Human Review Using AWS Step Functions + Slack
- Automated workflows often need human decisions, such as cloud resource approval or model deployment.
- Implementing a manual approval system using AWS Step Functions, Slack, Lambda, and SNS is discussed.
- The architecture allows pausing workflows for human approvals without unnecessary compute usage.
- The solution involves AWS Step Functions for business logic execution, Slack notifications, and human interactions.
- The process includes invoking Lambda functions, setting up SNS topics, Slack workflows, and API Gateway endpoints.
- Slack messages are sent with approval links, and Lambda functions handle user responses.
- Testing and considerations for timeouts, context passing, and Slack channel management are highlighted.
- Lessons learned include setting timeouts, managing permissions, and securing URLs shared in Slack messages.
- The approach of using Step Functions and Slack ensures traceable and production-safe approval workflows.
- The method offers a reliable way to incorporate human judgment in critical workflows efficiently.
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VentureBeat
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Sakana introduces new AI architecture, ‘Continuous Thought Machines’ to make models reason with less guidance — like human brains
- Tokyo-based startup Sakana, co-founded by ex-Google AI scientists, introduces Continuous Thought Machines (CTM) for flexible AI reasoning closer to human minds.
- CTMs enable diverse cognitive tasks without fixed parallel processing, instead unfolding computation per input/output unit.
- Each CTM neuron retains a memory for deciding activation, adjusting reasoning dynamically based on task complexity.
- CTMs differ from Transformer models by allowing neurons to operate on an internal timeline with variable computation depth.
- Sakana's aim is brain-like adaptability with competence exceeding human capabilities, using novel CTM mechanisms for reasoning.
- CTMs achieve competitive accuracy on benchmarks like ImageNet-1K, demonstrating sequential reasoning and natural calibration.
- Sakana AI's CTM architecture, though experimental, offers interpretability and adaptability across tasks like image classification and maze-solving.
- CTMs need further optimization for commercial deployment, demanding more resources than standard transformer models.
- Despite resource challenges, Sakana's open-sourced CTM implementation on GitHub encourages exploration and research across various domains.
- CTMs offer valuable trade-offs in trust, interpretability, and reasoning flow, making them a potential asset for production systems.
- Sakana's philosophy of adaptive models and transparency in AI development challenges the status quo, emphasizing evolution and collaboration.
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VentureBeat
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OpenAI just fixed ChatGPT’s most annoying business problem: meet the PDF export that changes everything
- OpenAI has introduced a new PDF export feature for its Deep Research tool, catering to enterprise customers and emphasizing on packaging capabilities for specific business problems.
- The PDF export enables users to download research reports with preserved formatting, tables, images, and clickable citations, targeting professional users who need to share polished research.
- This strategic move showcases OpenAI's shift towards enterprise markets and recognition of the importance of practical features over raw technical performance.
- Competitors like Perplexity and You.com have also entered the AI research assistant market with features focusing on speed, comprehensiveness, and workflow integration.
- The rapid evolution in AI product development is emphasizing user experience and integration for enterprise tools, rather than just technical capabilities.
- PDF export addresses critical enterprise adoption requirements by bridging the gap between AI and traditional business communication, verifiability, and shareability.
- OpenAI's focus on seamless integration into existing workflows with practical features signifies a mature phase in AI tool evolution towards practical business applications.
- The significance lies in how AI tools can be effectively leveraged within organizations by addressing specific workflow problems with minimal disruption.
- In enterprise markets, the balance between innovation and practicality is crucial for AI vendors to drive widespread adoption within organizations.
- The introduction of PDF export for Deep Research reflects OpenAI's strategic positioning to cater to enterprise needs by focusing on usability and integration into existing processes.
- As AI tools advance, features that ease integration into daily work processes become key drivers for adoption, highlighting the importance of packaging AI capabilities effectively.
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Medium
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How I Made Cinematic Movies with AI and You Can Too!
- AI MovieMaker is an innovative tool that enables users to create ultra-realistic 8K cinematic movies effortlessly.
- The software combines advanced AI technology with user-friendly features, making professional-level filmmaking accessible to everyone.
- Users, like Sarah, have experienced success in creating short films and receiving positive feedback, leading to potential business opportunities.
- AI MovieMaker has empowered individuals to turn their passion for filmmaking into profitable ventures, offering endless possibilities for creativity and growth.
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Medium
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Exploring the Reality of AI-Induced Job Displacement
- AI's impact on the job market is a contentious issue, evoking both excitement and apprehension.
- While automation has raised concerns about job security, AI also presents opportunities for empowerment and skill enhancement.
- AI has the potential to create new job roles and enhance existing ones, rather than solely displacing human workers.
- Exploring AI's influence on employment and future workforce trends can provide a deeper understanding of the evolving job landscape.
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Medium
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How We Met AI: Episode 1 : RNN Encoder–Decoder Revolution
- The blog series explores the evolution of LLMs and generative AI, starting from encoder-decoder models to advanced GPT frameworks and autonomous AI agents.
- Automated translation progressed from rule-based systems to neural networks like RNNs, designed for processing sequential data with a hidden state for carrying forward information from previous steps.
- The RNN encoder-decoder model aimed to address the challenge of language translation but had limitations with long and complex sentences due to its sequential nature.
- The evolution from rule-based systems to RNN-based encoder-decoder models is discussed, highlighting their power and limitations, leading to innovations like LSTM and GRU for addressing challenges.
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Towards Data Science
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Running Python Programs in Your Browser
- WebAssembly (WASM) technology enables running Python code directly in the browser, expanding web capabilities beyond HTML, CSS, and JavaScript.
- Pyodide library leverages WebAssembly, benefiting Python developers by allowing execution of Python code in the browser.
- Benefits of using Pyodide include access to popular Python libraries like NumPy, Pandas, Scikit-learn, and Matplotlib for data science and machine learning tasks.
- Pyodide facilitates building interactive dashboards and tools by combining Python's processing power with web technologies like HTML, CSS, and JavaScript.
- WebAssembly offers portability, high performance, security, and allows developers to write code in languages like C, C++, Rust, and Python, providing near-native execution speed.
- Common use cases of WebAssembly include high-performance web apps, porting legacy code, multimedia processing, scientific computing, and running multiple languages like Python in the browser.
- Pyodide's integration with WebAssembly enables running Python in the browser, offering benefits such as extensive library ecosystem usage, enhanced responsiveness, and simplified deployment.
- The Pyodide project involves porting the CPython interpreter to WebAssembly, allowing for a functional Python interpreter optimized for web environments.
- Code examples demonstrate running Python code in the browser using Pyodide, including simple print statements, mathematical calculations, calling Python functions from JavaScript, and utilizing libraries like NumPy and Matplotlib.
- Further examples showcase running Pyodide in a Web Worker for heavy computations, creating data dashboards directly in the browser, and visualizing data using Numpy and Matplotlib.
- The combination of Python, Pyodide, and WebAssembly offers a powerful toolset for developers to run Python programs within browsers, opening up possibilities for interactive web applications.
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